AI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methods

项目摘要 阿尔茨海默病和阿尔茨海默病相关痴呆（AD/ADRD）是神经退行性疾病 以逐渐丧失认知和其他神经行为症状为特征。在美国，有 大约580万患者患有AD/ADRD，并且这些患者中的97%年龄大于65岁。在过去 十年来，真实世界数据（RWD），包括电子健康记录（EHR）数据和索赔数据，正在成为 对AD的药物再利用越来越有价值。 在这个提议中，我们计划开发新的高维半监督学习和主动学习 方法，以及相关的高维假设检验程序，以加速干预， AD/ADRD，使用AD/ADRD的药物再利用作为激励用例。我们将解决几个关键问题， 现实世界数据中的挑战，包括风险因素的高维性，合并用药， 以及AD/ADRD的复杂和异质性进展轨迹。该项目的成功将导致 用于促进AD/ADRD的药物再利用的新型机器学习和统计学习方法， 真实世界的数据。

PROJECT SUMMARY Alzheimer's Disease and Alzheimer's Disease Related Dementias (AD/ADRD) are neurodegenerative diseases characterized by progressive loss of cognition and other neurobehavioral symptoms. In United States, there are approximately 5.8 million patients live with AD/ADRD, and 97% of these patients are older than 65. In the last decade, real-world data (RWD), including electronic health records (EHR) data and claims data, are becoming increasingly valuable for drug repurposing of AD. In this proposal, we plan to develop novel high dimensional semi-supervised learning and active learning methods, as well as associated high dimensional hypothesis testing procedures, to accelerate interventions of AD/ADRD, using drug repurposing of AD/ADRD as a motivating use case. We will address several key challenges in real-world data including the high dimensionality of the risk factors, concomitant medication use, and complex and heterogeneous progression trajectories of AD/ADRD. The success of this project will lead to novel machine learning and statistical learning methods for facilitating drug repurposing for AD/ADRD based on real-world data.